LamantinAI/kaeru — Cognitive memory for LLM agents — local-first, and shareable across a team through the cloud.

Description and use

**Project Summary:**
LamantinAI/kaeru is an open-source project that provides a cognitive memory system for Large Language Model (LLM) agents, enabling local-first operation and seamless sharing across teams through the cloud. This project helps integrate isolated prompts and tools into repeatable workflows, streamlining multi-agent coordination and standardizing agent memory. With its straightforward integration and production-ready features, LamantinAI/kaeru is suitable for prototyping and internal workflows.

**Value Proposition:**
The primary value of LamantinAI/kaeru lies in its ability to turn isolated prompts and tools into repeatable workflows, making it easier to coordinate multi-agent operations, add tool-use pipelines, and standardize agent memory. This project enables teams to build more efficient and scalable workflows, ultimately improving productivity and reducing the complexity of LLM agent management.

**Practical Adoption Path:**
To adopt LamantinAI/kaeru, follow these steps:

1. **Evaluate the project**: Assess the project's features, documentation, and community support to ensure it aligns with your needs.
2. **Integrate with your LLM agents**: Use the exposed API/SDK/CLI to integrate LamantinAI/kaeru with your existing LLM agents.
3. **Configure and

Problem

LamantinAI/kaeru helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

Cognitive memory for LLM agents — local-first, and shareable across a team through the cloud.

Source